import pandas as pd
import numpy as np
import plotly.express as px

Data

df = pd.read_csv('Salon_Data.csv')
df.head(10)

	date	time	haircut	price	hair_stylist	gender	type	rating
0	01-07-2021	morning	hair	150	Asim	Male	regular	4.0
1	01-07-2021	morning	hair	250	Viraj	Female	new	4.7
2	01-07-2021	noon	beard	100	Atif	Male	regular	4.2
3	01-07-2021	noon	hair	150	Asim	Male	new	3.7
4	01-07-2021	noon	all	350	Viraj	Male	regular	4.8
5	01-07-2021	mid noon	beard	100	Atif	Male	regular	4.1
6	01-07-2021	mid noon	all	300	Asim	Male	regular	4.3
7	01-07-2021	noon	all	300	Asim	Male	regular	4.3
8	01-07-2021	morning	hair	150	Asim	Male	regular	4.0
9	01-07-2021	noon	hair	250	Viraj	Female	new	4.7

from plotly.offline import init_notebook_mode
init_notebook_mode()

Analysis with Scatter plot.

fig = px.scatter(df, x="rating", color='hair_stylist', y="date", range_x=[3.5,5], range_y=[0,13],template='plotly_dark', animation_frame = 'gender', size_max=20)
fig.show()

fig = px.scatter_3d(df, x='rating', y='date', z=df['haircut'].replace({'all':1,'beard':2, 'hair':3}),
                     symbol='hair_stylist')
fig.show()

fig = px.box(df, y="price",points='all')
fig.show()

date_count = df.date.value_counts().sort_index()
date_count

01-07-2021    30
02-07-2021    21
03-07-2021    21
04-07-2021    30
05-07-2021    35
06-07-2021    29
07-07-2021    30
08-07-2021    25
09-07-2021    17
10-07-2021    27
11-07-2021    33
12-07-2021    47
Name: date, dtype: int64

Understanding Sales of haircut.

fig = px.line(df, x=date_count.keys(), y=date_count, title='Sales of haircut')
fig.show()

Analysis of Sales with respect to price.

fig = px.violin(df, y=df.price, title= 'Sales with respect to price')
fig.show()

Price Vs Rating

fig = px.scatter(df, x="rating", y="price", color="rating", marginal_y="violin",
           marginal_x="box", trendline="ols", template="simple_white",
           title='Price vs Rating')
fig.show()

Analysis of haircut using pie chart.

The amount earned is approximatley same of all hairstylist.

fig = px.pie(df, values='price', names='hair_stylist', color='hair_stylist', hole=.3, color_discrete_map={'Atif':'#1261A0','Asim':'#072F5F','Viraj':'#3895D3'})
fig.show()

Using rating on all hairstylist we know that Viraj is best and Atif, Asim are equaly competing each other

fig = px.pie(df, values='rating', names='hair_stylist', color='hair_stylist', hole=0.3, color_discrete_map={'Atif':'#1261A0','Asim':'#072F5F','Viraj':'#3895D3'})
fig.show()

The below graph shows that, There are more men haircuts than women, which also states that men vist frequently than women.

fig = px.pie(df, values='price', names='gender', color='gender', color_discrete_map={'Male':'#006279', 'Female':'#abc0bb'})
fig.show()

The below graph states that.

1. Maximum haircut are of 'All' and 'hair' type
2. Less men prefer to trim 'beard' only at salon
3. Since men are frequent the number of haircuts are most

fig = px.pie(df, values='price', names='haircut', color='haircut', hole=0.3, color_discrete_map={'all':'#1261A0','hair':'#072F5F','beard':'#3895D3'})
fig.show()

73.4% customer prefer their regular hairstyle and 26.6% prefer something new.

fig = px.pie(df, values='price', names='type', color='type', color_discrete_map={'new':'#006279', 'regular':'#abc0bb'})
fig.show()

Co-relation of price and rating.

The below graph is intereactive visual representation of density heatmap between price and rating.

we can see that, 41 ratings with value '4.1' are given to price ranging between 150-190.

fig = px.density_heatmap(df, x="rating", y="price", marginal_x="histogram", marginal_y="histogram", color_continuous_scale=px.colors.sequential.Greys)
fig.show()